----------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\xahsce\Desktop\PhD Economics\Projects\Tax SSA Project\Data\ReplicationLog.log
  log type:  text
 opened on:  28 Oct 2025, 20:15:58

. 
. use "ReplicationData.dta", replace 

. 
end of do-file

. do "C:\Users\xahsce\AppData\Local\Temp\STD2448_000001.tmp"

. *///////////////////// MAIN PAPER //////////////////////
> 
. *-------------------------------------Table 2
. 
. 
. *Column 1-3
. 
. 
. reg PayTax ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute iib0.DetectionAttribute i
> b0.FineAttribute ib0.GenderAttribute ib0.EarningsAttribute, robust 

Linear regression                               Number of obs     =      2,026
                                                F(8, 2017)        =       6.13
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0232
                                                Root MSE          =     .48601

---------------------------------------------------------------------------------------------
                            |               Robust
                     PayTax | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |  -.0395603    .021652    -1.83   0.068     -.082023    .0029023
                            |
  ServiceProvisionAttribute |
               Good Access  |   .0491681   .0216754     2.27   0.023     .0066596    .0916767
                            |
          FairnessAttribute |
                   Equally  |   .0340925   .0216355     1.58   0.115    -.0083378    .0765227
                            |
      TransparencyAttribute |
                  A Lot Of  |   .0050385   .0216097     0.23   0.816    -.0373411    .0474182
                            |
         DetectionAttribute |
                    Likely  |  -.0592228    .021613    -2.74   0.006    -.1016089   -.0168367
                            |
              FineAttribute |
                Large Fine  |  -.0186875   .0216304    -0.86   0.388    -.0611078    .0237329
                            |
            GenderAttribute |
                    Female  |  -.0412438   .0216488    -1.91   0.057    -.0837002    .0012127
                            |
          EarningsAttribute |
               120 000 KSH  |   .1059639   .0216726     4.89   0.000     .0634608     .148467
                      _cons |   .5755837   .0331573    17.36   0.000     .5105576    .6406097
---------------------------------------------------------------------------------------------

. est store reg1 

. 
. reg Justifiable ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute iib0.DetectionAttrib
> ute ib0.FineAttribute ib0.GenderAttribute ib0.EarningsAttribute, robust 

Linear regression                               Number of obs     =      1,990
                                                F(8, 1981)        =       0.60
                                                Prob > F          =     0.7783
                                                R-squared         =     0.0024
                                                Root MSE          =     .49939

---------------------------------------------------------------------------------------------
                            |               Robust
                Justifiable | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |    .014202   .0224526     0.63   0.527    -.0298312    .0582351
                            |
  ServiceProvisionAttribute |
               Good Access  |   .0196387   .0224306     0.88   0.381    -.0243514    .0636287
                            |
          FairnessAttribute |
                   Equally  |  -.0088206   .0224212    -0.39   0.694    -.0527923     .035151
                            |
      TransparencyAttribute |
                  A Lot Of  |   .0025332   .0224083     0.11   0.910     -.041413    .0464794
                            |
         DetectionAttribute |
                    Likely  |   -.015493   .0224208    -0.69   0.490    -.0594639    .0284779
                            |
              FineAttribute |
                Large Fine  |  -.0131957   .0224236    -0.59   0.556    -.0571719    .0307806
                            |
            GenderAttribute |
                    Female  |   .0252645   .0224729     1.12   0.261    -.0188084    .0693375
                            |
          EarningsAttribute |
               120 000 KSH  |  -.0281713   .0224581    -1.25   0.210    -.0722152    .0158727
                      _cons |    .468272   .0343823    13.62   0.000     .4008427    .5357014
---------------------------------------------------------------------------------------------

. est store reg2 

. 
. reg TrustGov ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute iib0.DetectionAttribute
>  ib0.FineAttribute ib0.GenderAttribute ib0.EarningsAttribute, robust 

Linear regression                               Number of obs     =      1,995
                                                F(8, 1986)        =       2.02
                                                Prob > F          =     0.0406
                                                R-squared         =     0.0080
                                                Root MSE          =     .49577

---------------------------------------------------------------------------------------------
                            |               Robust
                   TrustGov | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |  -.0276046   .0222637    -1.24   0.215    -.0712672     .016058
                            |
  ServiceProvisionAttribute |
               Good Access  |   .0447743   .0222491     2.01   0.044     .0011402    .0884083
                            |
          FairnessAttribute |
                   Equally  |   .0178398   .0222212     0.80   0.422    -.0257394    .0614191
                            |
      TransparencyAttribute |
                  A Lot Of  |   .0173963   .0222205     0.78   0.434    -.0261816    .0609742
                            |
         DetectionAttribute |
                    Likely  |  -.0164968    .022235    -0.74   0.458    -.0601032    .0271095
                            |
              FineAttribute |
                Large Fine  |   .0405515    .022242     1.82   0.068    -.0030685    .0841716
                            |
            GenderAttribute |
                    Female  |  -.0505963   .0222717    -2.27   0.023    -.0942746    -.006918
                            |
          EarningsAttribute |
               120 000 KSH  |   .0132244   .0222668     0.59   0.553    -.0304443    .0568931
                      _cons |   .4221611   .0340157    12.41   0.000     .3554509    .4888714
---------------------------------------------------------------------------------------------

. est store reg3

. 
. *Column 4-6, Restricting analysis to people who report to have enough income do be eligible to pay tax 
. 
. reg PayTax ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute iib0.DetectionAttribute i
> b0.FineAttribute  ib0.GenderAttribute ib0.EarningsAttribute if EnoughIncome==1, robust 

Linear regression                               Number of obs     =        397
                                                F(8, 388)         =       2.79
                                                Prob > F          =     0.0052
                                                R-squared         =     0.0507
                                                Root MSE          =     .47784

---------------------------------------------------------------------------------------------
                            |               Robust
                     PayTax | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |  -.1275686   .0481478    -2.65   0.008    -.2222319   -.0329054
                            |
  ServiceProvisionAttribute |
               Good Access  |   .1138466   .0483556     2.35   0.019     .0187748    .2089183
                            |
          FairnessAttribute |
                   Equally  |  -.0074342   .0485782    -0.15   0.878    -.1029437    .0880753
                            |
      TransparencyAttribute |
                  A Lot Of  |  -.0233295   .0486785    -0.48   0.632    -.1190361     .072377
                            |
         DetectionAttribute |
                    Likely  |     -.0629    .048317    -1.30   0.194     -.157896     .032096
                            |
              FineAttribute |
                Large Fine  |  -.0593896   .0484076    -1.23   0.221    -.1545637    .0357845
                            |
            GenderAttribute |
                    Female  |  -.0408698   .0484793    -0.84   0.400    -.1361848    .0544453
                            |
          EarningsAttribute |
               120 000 KSH  |   .0977865   .0483699     2.02   0.044     .0026867    .1928864
                      _cons |   .6797521   .0769466     8.83   0.000     .5284675    .8310366
---------------------------------------------------------------------------------------------

. 
. est store reg4

. 
. reg Justifiable ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute iib0.DetectionAttrib
> ute ib0.FineAttribute  ib0.GenderAttribute ib0.EarningsAttribute if EnoughIncome==1, robust 

Linear regression                               Number of obs     =        391
                                                F(8, 382)         =       0.87
                                                Prob > F          =     0.5391
                                                R-squared         =     0.0177
                                                Root MSE          =     .49886

---------------------------------------------------------------------------------------------
                            |               Robust
                Justifiable | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |   -.055815   .0510803    -1.09   0.275    -.1562488    .0446188
                            |
  ServiceProvisionAttribute |
               Good Access  |   .0871952   .0510373     1.71   0.088     -.013154    .1875443
                            |
          FairnessAttribute |
                   Equally  |   .0091454   .0512198     0.18   0.858    -.0915627    .1098535
                            |
      TransparencyAttribute |
                  A Lot Of  |  -.0219589   .0509839    -0.43   0.667    -.1222032    .0782854
                            |
         DetectionAttribute |
                    Likely  |  -.0517917   .0506086    -1.02   0.307     -.151298    .0477146
                            |
              FineAttribute |
                Large Fine  |  -.0323475   .0509747    -0.63   0.526    -.1325736    .0678785
                            |
            GenderAttribute |
                    Female  |  -.0239783   .0513333    -0.47   0.641    -.1249095    .0769529
                            |
          EarningsAttribute |
               120 000 KSH  |  -.0313663   .0509519    -0.62   0.539    -.1315475     .068815
                      _cons |   .5111688   .0809227     6.32   0.000      .352059    .6702786
---------------------------------------------------------------------------------------------

. 
. est store reg5 

. 
. reg TrustGov ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute iib0.DetectionAttribute
>  ib0.FineAttribute  ib0.GenderAttribute ib0.EarningsAttribute if EnoughIncome==1, robust 

Linear regression                               Number of obs     =        392
                                                F(8, 383)         =       2.06
                                                Prob > F          =     0.0385
                                                R-squared         =     0.0405
                                                Root MSE          =     .48885

---------------------------------------------------------------------------------------------
                            |               Robust
                   TrustGov | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |  -.0645289   .0499253    -1.29   0.197    -.1626909    .0336331
                            |
  ServiceProvisionAttribute |
               Good Access  |   .1748897   .0499321     3.50   0.001     .0767143    .2730652
                            |
          FairnessAttribute |
                   Equally  |   .0383507   .0501884     0.76   0.445    -.0603286    .1370301
                            |
      TransparencyAttribute |
                  A Lot Of  |   .0598281   .0500957     1.19   0.233     -.038669    .1583252
                            |
         DetectionAttribute |
                    Likely  |   .0022576   .0495926     0.05   0.964    -.0952503    .0997654
                            |
              FineAttribute |
                Large Fine  |   .0406113   .0497761     0.82   0.415    -.0572574    .1384799
                            |
            GenderAttribute |
                    Female  |  -.0233736   .0503229    -0.46   0.643    -.1223173    .0755701
                            |
          EarningsAttribute |
               120 000 KSH  |  -.0276032   .0499675    -0.55   0.581    -.1258481    .0706417
                      _cons |   .3145524   .0792302     3.97   0.000     .1587718    .4703329
---------------------------------------------------------------------------------------------

. 
. est store reg6

. 
. 
. *Combining all regressions into one table
. esttab reg1 reg2 reg3 reg4 reg5 reg6  using maineffects.tex, se label replace booktabs ///
> alignment (D{.}{.}{-1}) ///
> title(Average Treatment Effects\label{Table:maineffects})
(output written to maineffects.tex)

. 
. 
. *////////////// APPENDIX //////////////////
> 
. 
. *----------------------------Table A1
. 
. * The table was created manually by observing the results from the following tabs 
. 
. tab GenderAttribute

 Female (vs |
      male) |      Freq.     Percent        Cum.
------------+-----------------------------------
       Male |      1,011       48.61       48.61
     Female |      1,069       51.39      100.00
------------+-----------------------------------
      Total |      2,080      100.00

. tab EarningsAttribute

      Earns |
120,000 (vs |
    30,000) |      Freq.     Percent        Cum.
------------+-----------------------------------
 30 000 KSH |      1,003       48.22       48.22
120 000 KSH |      1,077       51.78      100.00
------------+-----------------------------------
      Total |      2,080      100.00

. tab ServiceProvisionAttribute

   Good (vs |
      poor) |
  access to |
   services |      Freq.     Percent        Cum.
------------+-----------------------------------
Poor Access |        992       47.69       47.69
Good Access |      1,088       52.31      100.00
------------+-----------------------------------
      Total |      2,080      100.00

. tab LocalContributionsAttribute

     Large (vs |
        small) |
   donation to |
     community |
         goods |      Freq.     Percent        Cum.
---------------+-----------------------------------
Small Donation |      1,085       52.16       52.16
Large Donation |        995       47.84      100.00
---------------+-----------------------------------
         Total |      2,080      100.00

. tab FairnessAttribute

Equally (vs |
 unequally) |
distributed |
  resources |      Freq.     Percent        Cum.
------------+-----------------------------------
  Unequally |      1,035       49.76       49.76
    Equally |      1,045       50.24      100.00
------------+-----------------------------------
      Total |      2,080      100.00

. tab TransparencyAttribute

  A lot (vs |
    little) |
information |
  about tax |
   fund use |      Freq.     Percent        Cum.
------------+-----------------------------------
Very Little |      1,065       51.20       51.20
   A Lot Of |      1,015       48.80      100.00
------------+-----------------------------------
      Total |      2,080      100.00

. tab DetectionAttribute

 Likely (vs |
  unlikely) |
  detection |
     of tax |
    evasion |      Freq.     Percent        Cum.
------------+-----------------------------------
   Unlikely |      1,018       48.94       48.94
     Likely |      1,062       51.06      100.00
------------+-----------------------------------
      Total |      2,080      100.00

. tab FineAttribute

  Large (vs |
small) fine |
for evasion |      Freq.     Percent        Cum.
------------+-----------------------------------
 Small Fine |      1,080       51.92       51.92
 Large Fine |      1,000       48.08      100.00
------------+-----------------------------------
      Total |      2,080      100.00

. 
. *----------------------------Table A2
. 
. * The table was created manually by observing the results from the following summarize, detail
. 
. 
. sum PayTax, d

              Thinks person should pay the tax
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs               2,026
25%            0              0       Sum of wgt.       2,026

50%            1                      Mean           .5962488
                        Largest       Std. dev.      .4907699
75%            1              1
90%            1              1       Variance       .2408551
95%            1              1       Skewness      -.3923327
99%            1              1       Kurtosis       1.153925

. sum Justifiable, d 

       Thinks it is justifiable if person evades taxes
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs               1,990
25%            0              0       Sum of wgt.       1,990

50%            0                      Mean           .4663317
                        Largest       Std. dev.      .4989905
75%            1              1
90%            1              1       Variance       .2489916
95%            1              1       Skewness       .1349797
99%            1              1       Kurtosis        1.01822

. sum TrustGov, d

         Trusts government to use tax funds properly
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs               1,995
25%            0              0       Sum of wgt.       1,995

50%            0                      Mean           .4421053
                        Largest       Std. dev.      .4967614
75%            1              1
90%            1              1       Variance       .2467719
95%            1              1       Skewness       .2331471
99%            1              1       Kurtosis       1.054358

. sum RespDetection, d

       Respondent Perception: likelihood of detecting
                           evasion
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs               1,896
25%            0              0       Sum of wgt.       1,896

50%            1                      Mean           .5996835
                        Largest       Std. dev.      .4900917
75%            1              1
90%            1              1       Variance       .2401899
95%            1              1       Skewness      -.4069028
99%            1              1       Kurtosis        1.16557

. sum RespTaxBenefit, d

        espondent Perception: taxpayers benefit from
                  public goods and services
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs               1,960
25%            0              0       Sum of wgt.       1,960

50%            1                      Mean           .5265306
                        Largest       Std. dev.      .4994231
75%            1              1
90%            1              1       Variance       .2494234
95%            1              1       Skewness      -.1062722
99%            1              1       Kurtosis       1.011294

. sum RespCommunityGoods, d

       Respondent heard about  donation initiatives in
                 their community (past year)
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs               2,041
25%            0              0       Sum of wgt.       2,041

50%            0                      Mean           .3591377
                        Largest       Std. dev.      .4798652
75%            1              1
90%            1              1       Variance       .2302706
95%            1              1       Skewness       .5872351
99%            1              1       Kurtosis       1.344845

. sum RespContributed, d

             Respondent contributed (past year)
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs               2,053
25%            0              0       Sum of wgt.       2,053

50%            0                      Mean           .3989284
                        Largest       Std. dev.      .4897973
75%            1              1
90%            1              1       Variance       .2399014
95%            1              1       Skewness       .4128084
99%            1              1       Kurtosis       1.170411

. sum Female, d

                    Respondent is female
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs               2,080
25%            0              0       Sum of wgt.       2,080

50%            1                      Mean             .50625
                        Largest       Std. dev.      .5000812
75%            1              1
90%            1              1       Variance       .2500812
95%            1              1       Skewness       -.025002
99%            1              1       Kurtosis       1.000625

. sum Age, d

                   Respondent age (years)
-------------------------------------------------------------
      Percentiles      Smallest
 1%           18             18
 5%           20             18
10%           21             18       Obs               2,080
25%           24             18       Sum of wgt.       2,080

50%           30                      Mean           32.16442
                        Largest       Std. dev.      11.06598
75%           38             79
90%           48             79       Variance       122.4559
95%           55             80       Skewness        1.26425
99%           68             86       Kurtosis       4.539316

. sum Education, d

                     Years of education
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            2              1
10%            2              1       Obs               2,035
25%            3              1       Sum of wgt.       2,035

50%            3                      Mean           3.533661
                        Largest       Std. dev.       1.11934
75%            5              5
90%            5              5       Variance       1.252922
95%            5              5       Skewness      -.0979361
99%            5              5       Kurtosis       1.946606

. sum Works, d

                     Currently employed
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs               2,031
25%            0              0       Sum of wgt.       2,031

50%            0                      Mean           .4756278
                        Largest       Std. dev.      .4995286
75%            1              1
90%            1              1       Variance       .2495289
95%            1              1       Skewness       .0976049
99%            1              1       Kurtosis       1.009527

. sum Rural, d

                     Lives in rural area
-------------------------------------------------------------
      Percentiles      Smallest
 1%            0              0
 5%            0              0
10%            0              0       Obs               2,080
25%            0              0       Sum of wgt.       2,080

50%            1                      Mean           .5668269
                        Largest       Std. dev.      .4956332
75%            1              1
90%            1              1       Variance       .2456523
95%            1              1       Skewness      -.2697277
99%            1              1       Kurtosis       1.072753

. 
. *----------------------------Table A3
. 
. * The table was created manually by observing the results from the following tabs
. 
. tab PayTaxRaw

  Raw measure of |
          PayTax |      Freq.     Percent        Cum.
-----------------+-----------------------------------
Refuse to Answer |          9        0.43        0.43
      Don�t Know |         45        2.16        2.60
             Yes |      1,208       58.08       60.67
              No |        818       39.33      100.00
-----------------+-----------------------------------
           Total |      2,080      100.00

. tab JustifiableRaw

  Raw measure of |
     Justifiable |      Freq.     Percent        Cum.
-----------------+-----------------------------------
Refuse to Answer |         14        0.67        0.67
      Don�t Know |         76        3.65        4.33
             Yes |        928       44.62       48.94
              No |      1,062       51.06      100.00
-----------------+-----------------------------------
           Total |      2,080      100.00

. tab TrustRaw

  Raw measure of |
        TrustGov |      Freq.     Percent        Cum.
-----------------+-----------------------------------
Refuse to Answer |         15        0.72        0.72
      Don�t Know |         70        3.37        4.09
             Yes |        882       42.40       46.49
              No |      1,113       53.51      100.00
-----------------+-----------------------------------
           Total |      2,080      100.00

. *----------------------------Table A4
. 
. logit PayTax ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute iib0.DetectionAttribute
>  ib0.FineAttribute ib0.GenderAttribute ib0.EarningsAttribute, robust 

Iteration 0:  Log pseudolikelihood = -1366.5438  
Iteration 1:  Log pseudolikelihood = -1342.8739  
Iteration 2:  Log pseudolikelihood = -1342.8455  
Iteration 3:  Log pseudolikelihood = -1342.8455  

Logistic regression                                     Number of obs =  2,026
                                                        Wald chi2(8)  =  45.88
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -1342.8455                       Pseudo R2     = 0.0173

---------------------------------------------------------------------------------------------
                            |               Robust
                     PayTax | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |   -.168175   .0917763    -1.83   0.067    -.3480531    .0117032
                            |
  ServiceProvisionAttribute |
               Good Access  |   .2086661   .0917944     2.27   0.023     .0287524    .3885799
                            |
          FairnessAttribute |
                   Equally  |    .144371   .0918163     1.57   0.116    -.0355856    .3243276
                            |
      TransparencyAttribute |
                  A Lot Of  |   .0211416   .0917102     0.23   0.818     -.158607    .2008902
                            |
         DetectionAttribute |
                    Likely  |  -.2515153   .0918259    -2.74   0.006    -.4314907   -.0715398
                            |
              FineAttribute |
                Large Fine  |  -.0793578   .0917557    -0.86   0.387    -.2591956    .1004801
                            |
            GenderAttribute |
                    Female  |  -.1761589   .0920425    -1.91   0.056    -.3565589    .0042411
                            |
          EarningsAttribute |
               120 000 KSH  |   .4469622   .0917638     4.87   0.000     .2671084     .626816
                      _cons |   .3141963   .1397425     2.25   0.025     .0403059    .5880866
---------------------------------------------------------------------------------------------

. 
. est store reg1 

. 
. logit Justifiable ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute iib0.DetectionAttr
> ibute ib0.FineAttribute ib0.GenderAttribute ib0.EarningsAttribute, robust 

Iteration 0:  Log pseudolikelihood = -1374.8479  
Iteration 1:  Log pseudolikelihood = -1372.4411  
Iteration 2:  Log pseudolikelihood = -1372.4411  

Logistic regression                                     Number of obs =  1,990
                                                        Wald chi2(8)  =   4.78
                                                        Prob > chi2   = 0.7804
Log pseudolikelihood = -1372.4411                       Pseudo R2     = 0.0018

---------------------------------------------------------------------------------------------
                            |               Robust
                Justifiable | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |   .0572049   .0902488     0.63   0.526    -.1196795    .2340892
                            |
  ServiceProvisionAttribute |
               Good Access  |   .0790749   .0901908     0.88   0.381     -.097696    .2558457
                            |
          FairnessAttribute |
                   Equally  |  -.0355251   .0901246    -0.39   0.693     -.212166    .1411158
                            |
      TransparencyAttribute |
                  A Lot Of  |   .0102055   .0900747     0.11   0.910    -.1663377    .1867488
                            |
         DetectionAttribute |
                    Likely  |  -.0623582   .0901239    -0.69   0.489    -.2389979    .1142814
                            |
              FineAttribute |
                Large Fine  |  -.0531707   .0901501    -0.59   0.555    -.2298618    .1235203
                            |
            GenderAttribute |
                    Female  |   .1017117   .0903722     1.13   0.260    -.0754145    .2788379
                            |
          EarningsAttribute |
               120 000 KSH  |  -.1133745   .0902657    -1.26   0.209    -.2902921     .063543
                      _cons |  -.1274111   .1381243    -0.92   0.356    -.3981298    .1433075
---------------------------------------------------------------------------------------------

. est store reg2

. 
. logit TrustGov ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute iib0.DetectionAttribu
> te ib0.FineAttribute ib0.GenderAttribute ib0.EarningsAttribute, robust 

Iteration 0:  Log pseudolikelihood = -1369.4249  
Iteration 1:  Log pseudolikelihood = -1361.4204  
Iteration 2:  Log pseudolikelihood = -1361.4191  
Iteration 3:  Log pseudolikelihood = -1361.4191  

Logistic regression                                     Number of obs =  1,995
                                                        Wald chi2(8)  =  15.79
                                                        Prob > chi2   = 0.0454
Log pseudolikelihood = -1361.4191                       Pseudo R2     = 0.0058

---------------------------------------------------------------------------------------------
                            |               Robust
                   TrustGov | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |  -.1127994   .0908559    -1.24   0.214    -.2908736    .0652748
                            |
  ServiceProvisionAttribute |
               Good Access  |    .182895   .0908444     2.01   0.044     .0048433    .3609467
                            |
          FairnessAttribute |
                   Equally  |   .0728807   .0906296     0.80   0.421    -.1047501    .2505114
                            |
      TransparencyAttribute |
                  A Lot Of  |    .071019   .0906198     0.78   0.433    -.1065926    .2486305
                            |
         DetectionAttribute |
                    Likely  |  -.0674825    .090688    -0.74   0.457    -.2452278    .1102627
                            |
              FineAttribute |
                Large Fine  |   .1655893   .0907222     1.83   0.068     -.012223    .3434016
                            |
            GenderAttribute |
                    Female  |  -.2063959   .0908393    -2.27   0.023    -.3844376   -.0283541
                            |
          EarningsAttribute |
               120 000 KSH  |   .0539276   .0908354     0.59   0.553    -.1241065    .2319617
                      _cons |  -.3159701   .1388324    -2.28   0.023    -.5880765   -.0438636
---------------------------------------------------------------------------------------------

. est store reg3

. 
. 
. *Combining all regressions into one table
. esttab reg1 reg2 reg3  using mainlogistic.tex, se label replace booktabs ///
> alignment (D{.}{.}{-1}) ///
> title( Average Treatment Effects Logistic Regression \label{Table:mainlogistic})
(output written to mainlogistic.tex)

. 
. *----------------------------Table A5 
. 
. reg LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute ib0.DetectionAttribute ib0.FineAttri
> bute  ib0.GenderAttribute ib0.EarningsAttribute, robust

Linear regression                               Number of obs     =      2,080
                                                F(7, 2072)        =       1.21
                                                Prob > F          =     0.2941
                                                R-squared         =     0.0041
                                                Root MSE          =     .49948

-------------------------------------------------------------------------------------------
                          |               Robust
LocalContributionsAttri~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
ServiceProvisionAttribute |
             Good Access  |  -.0352977   .0219379    -1.61   0.108    -.0783202    .0077248
                          |
        FairnessAttribute |
                 Equally  |   .0036899   .0219298     0.17   0.866    -.0393168    .0466966
                          |
    TransparencyAttribute |
                A Lot Of  |   .0081511   .0219231     0.37   0.710    -.0348426    .0511448
                          |
       DetectionAttribute |
                  Likely  |  -.0163297   .0219321    -0.74   0.457     -.059341    .0266816
                          |
            FineAttribute |
              Large Fine  |   .0126743   .0219444     0.58   0.564    -.0303611    .0557098
                          |
          GenderAttribute |
                  Female  |  -.0384339   .0219666    -1.75   0.080    -.0815127     .004645
                          |
        EarningsAttribute |
             120 000 KSH  |  -.0278353   .0219722    -1.27   0.205    -.0709252    .0152545
                    _cons |   .5274072    .031548    16.72   0.000     .4655381    .5892762
-------------------------------------------------------------------------------------------

. 
. est store reg1

. 
. reg ServiceProvisionAttribute ib0.LocalContributionsAttribute ib0.FairnessAttribute ib0.TransparencyAttribute iib0.DetectionAttribute ib0.FineAttr
> ibute  ib0.GenderAttribute ib0.EarningsAttribute, robust

Linear regression                               Number of obs     =      2,080
                                                F(7, 2072)        =       0.57
                                                Prob > F          =     0.7770
                                                R-squared         =     0.0019
                                                Root MSE          =     .49995

---------------------------------------------------------------------------------------------
                            |               Robust
  ServiceProvisionAttribute | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |  -.0353642   .0219795    -1.61   0.108    -.0784684    .0077399
                            |
          FairnessAttribute |
                   Equally  |  -.0147409   .0219474    -0.67   0.502    -.0577821    .0283003
                            |
      TransparencyAttribute |
                  A Lot Of  |   .0021169   .0219403     0.10   0.923    -.0409104    .0451443
                            |
         DetectionAttribute |
                    Likely  |   .0085637   .0219537     0.39   0.697    -.0344898    .0516172
                            |
              FineAttribute |
                Large Fine  |  -.0053935   .0219652    -0.25   0.806    -.0484697    .0376828
                            |
            GenderAttribute |
                    Female  |  -.0124185   .0219898    -0.56   0.572    -.0555428    .0307059
                            |
          EarningsAttribute |
               120 000 KSH  |   .0147426   .0219908     0.67   0.503    -.0283838     .057869
                      _cons |   .5433362   .0313866    17.31   0.000     .4817836    .6048889
---------------------------------------------------------------------------------------------

. 
. est store reg2

. 
. reg FairnessAttribute ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.TransparencyAttribute iib0.DetectionAttribute ib0.FineAttr
> ibute  ib0.GenderAttribute ib0.EarningsAttribute, robust

Linear regression                               Number of obs     =      2,080
                                                F(7, 2072)        =       0.70
                                                Prob > F          =     0.6724
                                                R-squared         =     0.0023
                                                Root MSE          =     .50037

---------------------------------------------------------------------------------------------
                            |               Robust
          FairnessAttribute | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |   .0037031   .0220082     0.17   0.866    -.0394574    .0468637
                            |
  ServiceProvisionAttribute |
               Good Access  |  -.0147658   .0219845    -0.67   0.502    -.0578798    .0283481
                            |
      TransparencyAttribute |
                  A Lot Of  |  -.0155768   .0219567    -0.71   0.478    -.0586362    .0274826
                            |
         DetectionAttribute |
                    Likely  |  -.0089534   .0219716    -0.41   0.684    -.0520421    .0341353
                            |
              FineAttribute |
                Large Fine  |   .0115691   .0219785     0.53   0.599    -.0315331    .0546712
                            |
            GenderAttribute |
                    Female  |  -.0410582   .0219968    -1.87   0.062    -.0841964      .00208
                            |
          EarningsAttribute |
               120 000 KSH  |  -.0006264   .0220219    -0.03   0.977    -.0438138    .0425609
                      _cons |   .5363925   .0314222    17.07   0.000     .4747702    .5980148
---------------------------------------------------------------------------------------------

. 
. est store reg3

. 
. reg TransparencyAttribute ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute  ib0.DetectionAttribute ib0.FineAttr
> ibute ib0.GenderAttribute ib0.EarningsAttribute, robust

Linear regression                               Number of obs     =      2,080
                                                F(7, 2072)        =       0.31
                                                Prob > F          =     0.9511
                                                R-squared         =     0.0010
                                                Root MSE          =     .50056

---------------------------------------------------------------------------------------------
                            |               Robust
      TransparencyAttribute | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |   .0081865   .0220184     0.37   0.710    -.0349939    .0513669
                            |
  ServiceProvisionAttribute |
               Good Access  |   .0021221   .0219941     0.10   0.923    -.0410107     .045255
                            |
          FairnessAttribute |
                   Equally  |  -.0155886   .0219732    -0.71   0.478    -.0586805    .0275033
                            |
         DetectionAttribute |
                    Likely  |  -.0103633     .02198    -0.47   0.637    -.0534685    .0327419
                            |
              FineAttribute |
                Large Fine  |  -.0205076   .0219869    -0.93   0.351    -.0636262    .0226111
                            |
            GenderAttribute |
                    Female  |   -.010231   .0220307    -0.46   0.642    -.0534355    .0329735
                            |
          EarningsAttribute |
               120 000 KSH  |  -.0080085    .022031    -0.36   0.716    -.0512136    .0351967
                      _cons |   .5153419   .0316631    16.28   0.000      .453247    .5774367
---------------------------------------------------------------------------------------------

. 
. est store reg4

. 
. reg DetectionAttribute ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute ib0.FineAttri
> bute ib0.GenderAttribute ib0.EarningsAttribute, robust

Linear regression                               Number of obs     =      2,080
                                                F(7, 2072)        =       0.65
                                                Prob > F          =     0.7129
                                                R-squared         =     0.0022
                                                Root MSE          =      .5003

---------------------------------------------------------------------------------------------
                            |               Robust
         DetectionAttribute | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |  -.0163837   .0220049    -0.74   0.457    -.0595377    .0267702
                            |
  ServiceProvisionAttribute |
               Good Access  |   .0085759   .0219848     0.39   0.697    -.0345387    .0516904
                            |
          FairnessAttribute |
                   Equally  |   -.008951   .0219655    -0.41   0.684    -.0520277    .0341258
                            |
      TransparencyAttribute |
                  A Lot Of  |  -.0103526   .0219573    -0.47   0.637    -.0534133    .0327081
                            |
              FineAttribute |
                Large Fine  |   .0058948   .0219752     0.27   0.789     -.037201    .0489907
                            |
            GenderAttribute |
                    Female  |  -.0027504   .0220146    -0.12   0.901    -.0459236    .0404227
                            |
          EarningsAttribute |
               120 000 KSH  |  -.0409622   .0219942    -1.86   0.063    -.0840953    .0021709
                      _cons |   .5432666   .0313906    17.31   0.000     .4817062     .604827
---------------------------------------------------------------------------------------------

. 
. est store reg5 

. 
. reg FineAttribute ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute ib0.DetectionAttri
> bute ib0.GenderAttribute ib0.EarningsAttribute, robust

Linear regression                               Number of obs     =      2,080
                                                F(7, 2072)        =       0.66
                                                Prob > F          =     0.7080
                                                R-squared         =     0.0022
                                                Root MSE          =     .50004

---------------------------------------------------------------------------------------------
                            |               Robust
              FineAttribute | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |   .0127029   .0219936     0.58   0.564     -.030429    .0558348
                            |
  ServiceProvisionAttribute |
               Good Access  |  -.0053955   .0219733    -0.25   0.806    -.0484875    .0376966
                            |
          FairnessAttribute |
                   Equally  |   .0115538   .0219495     0.53   0.599    -.0314915    .0545991
                            |
      TransparencyAttribute |
                  A Lot Of  |  -.0204649   .0219412    -0.93   0.351     -.063494    .0225641
                            |
         DetectionAttribute |
                    Likely  |   .0058887   .0219522     0.27   0.789     -.037162    .0489393
                            |
            GenderAttribute |
                    Female  |     .02853   .0219979     1.30   0.195    -.0146102    .0716702
                            |
          EarningsAttribute |
               120 000 KSH  |   .0246268   .0220014     1.12   0.263    -.0185204    .0677739
                      _cons |   .4512758   .0320537    14.08   0.000     .3884151    .5141366
---------------------------------------------------------------------------------------------

. 
. est store reg6

. 
. 
. reg GenderAttribute ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute ib0.DetectionAtt
> ribute ib0.FineAttribute ib0.EarningsAttribute, robust

Linear regression                               Number of obs     =      2,080
                                                F(7, 2072)        =       1.92
                                                Prob > F          =     0.0629
                                                R-squared         =     0.0064
                                                Root MSE          =     .49917

---------------------------------------------------------------------------------------------
                            |               Robust
            GenderAttribute | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |  -.0383873   .0219403    -1.75   0.080    -.0814146    .0046401
                            |
  ServiceProvisionAttribute |
               Good Access  |  -.0123801   .0219218    -0.56   0.572    -.0553712     .030611
                            |
          FairnessAttribute |
                   Equally  |  -.0408621   .0218922    -1.87   0.062    -.0837952    .0020709
                            |
      TransparencyAttribute |
                  A Lot Of  |  -.0101744   .0219091    -0.46   0.642    -.0531406    .0327917
                            |
         DetectionAttribute |
                    Likely  |  -.0027381   .0219155    -0.12   0.901    -.0457167    .0402406
                            |
              FineAttribute |
                Large Fine  |   .0284313    .021922     1.30   0.195    -.0145603    .0714228
                            |
          EarningsAttribute |
               120 000 KSH  |    .045195   .0219433     2.06   0.040     .0021619    .0882281
                      _cons |    .528603   .0314268    16.82   0.000     .4669717    .5902344
---------------------------------------------------------------------------------------------

. 
. est store reg7

. 
. reg EarningsAttribute ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute ib0.DetectionA
> ttribute ib0.FineAttribute ib0. GenderAttribute, robust

Linear regression                               Number of obs     =      2,080
                                                F(7, 2072)        =       1.64
                                                Prob > F          =     0.1201
                                                R-squared         =     0.0055
                                                Root MSE          =     .49927

---------------------------------------------------------------------------------------------
                            |               Robust
          EarningsAttribute | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------------------+----------------------------------------------------------------
LocalContributionsAttribute |
            Large Donation  |  -.0278121   .0219537    -1.27   0.205    -.0708657    .0152416
                            |
  ServiceProvisionAttribute |
               Good Access  |   .0147025   .0219308     0.67   0.503    -.0283061    .0577112
                            |
          FairnessAttribute |
                   Equally  |  -.0006237    .021925    -0.03   0.977     -.043621    .0423736
                            |
      TransparencyAttribute |
                  A Lot Of  |  -.0079672   .0219174    -0.36   0.716    -.0509495    .0350152
                            |
         DetectionAttribute |
                    Likely  |  -.0407931   .0219033    -1.86   0.063     -.083748    .0021618
                            |
              FineAttribute |
                Large Fine  |   .0245508   .0219336     1.12   0.263    -.0184633    .0675649
                            |
            GenderAttribute |
                    Female  |    .045212    .021951     2.06   0.040     .0021637    .0882604
                      _cons |   .5133917   .0316122    16.24   0.000     .4513967    .5753868
---------------------------------------------------------------------------------------------

. 
. est store reg8

. 
. *Combining all regressions into one table
. esttab reg1 reg2 reg3 reg4 reg5 reg6 reg7 reg8 using crossbalance.tex, se label replace booktabs ///
> alignment (D{.}{.}{-1}) ///
> title(Cross Balance\label{Table:crossbalance})
(output written to crossbalance.tex)

. 
. *----------------------------Table A6
. 
. reg LocalContributionsAttribute c.Age##c.Age Female EverEmployed EnoughIncome SecondaryEducation Rural, robust

Linear regression                               Number of obs     =      1,993
                                                F(7, 1985)        =       1.30
                                                Prob > F          =     0.2479
                                                R-squared         =     0.0045
                                                Root MSE          =     .49946

------------------------------------------------------------------------------------
                   |               Robust
LocalContributio~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
               Age |   .0041129   .0053684     0.77   0.444    -.0064154    .0146411
                   |
       c.Age#c.Age |  -.0000481   .0000658    -0.73   0.464    -.0001771    .0000808
                   |
            Female |   .0061249   .0232383     0.26   0.792    -.0394491    .0516989
      EverEmployed |   -.041995   .0257105    -1.63   0.103    -.0924173    .0084274
      EnoughIncome |   .0476146   .0301356     1.58   0.114    -.0114862    .1067154
SecondaryEducation |   .0570432   .0303989     1.88   0.061     -.002574    .1166603
             Rural |   .0178899   .0230566     0.78   0.438    -.0273279    .0631076
             _cons |   .3605581   .1038473     3.47   0.001     .1568969    .5642192
------------------------------------------------------------------------------------

. 
. est store reg1

. 
. reg ServiceProvisionAttribute c.Age##c.Age Female EverEmployed EnoughIncome SecondaryEducation Rural, robust

Linear regression                               Number of obs     =      1,993
                                                F(7, 1985)        =       1.58
                                                Prob > F          =     0.1374
                                                R-squared         =     0.0056
                                                Root MSE          =     .49889

------------------------------------------------------------------------------------
                   |               Robust
ServiceProvision~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
               Age |   .0057105   .0055916     1.02   0.307    -.0052556    .0166767
                   |
       c.Age#c.Age |  -.0000498   .0000691    -0.72   0.472    -.0001854    .0000858
                   |
            Female |   .0213793   .0231819     0.92   0.357    -.0240841    .0668427
      EverEmployed |   .0282332   .0256561     1.10   0.271    -.0220824    .0785489
      EnoughIncome |  -.0325855   .0299469    -1.09   0.277    -.0913162    .0261453
SecondaryEducation |   .0376638   .0303403     1.24   0.215    -.0218384     .097166
             Rural |  -.0516777    .023024    -2.24   0.025    -.0968313    -.006524
             _cons |   .3775466   .1071601     3.52   0.000     .1673885    .5877046
------------------------------------------------------------------------------------

. 
. est store reg2

. 
. reg FairnessAttribute c.Age##c.Age Female EverEmployed EnoughIncome SecondaryEducation Rural, robust

Linear regression                               Number of obs     =      1,993
                                                F(7, 1985)        =       1.85
                                                Prob > F          =     0.0737
                                                R-squared         =     0.0065
                                                Root MSE          =     .49939

------------------------------------------------------------------------------------
                   |               Robust
 FairnessAttribute | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
               Age |  -.0046725   .0055938    -0.84   0.404    -.0156428    .0062979
                   |
       c.Age#c.Age |   .0000568   .0000692     0.82   0.412     -.000079    .0001925
                   |
            Female |   .0208788   .0232754     0.90   0.370     -.024768    .0665255
      EverEmployed |  -.0320543   .0257976    -1.24   0.214    -.0826475     .018539
      EnoughIncome |   .0698389   .0300809     2.32   0.020     .0108455    .1288324
SecondaryEducation |   .0478651   .0303498     1.58   0.115    -.0116557     .107386
             Rural |  -.0275204   .0230308    -1.19   0.232    -.0726875    .0176466
             _cons |   .5566842    .106939     5.21   0.000     .3469597    .7664087
------------------------------------------------------------------------------------

. 
. est store reg3

. 
. reg TransparencyAttribute c.Age##c.Age Female EverEmployed EnoughIncome SecondaryEducation Rural, robust

Linear regression                               Number of obs     =      1,993
                                                F(7, 1985)        =       1.56
                                                Prob > F          =     0.1416
                                                R-squared         =     0.0053
                                                Root MSE          =     .49955

------------------------------------------------------------------------------------
                   |               Robust
TransparencyAttr~e | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
               Age |  -.0102578   .0053768    -1.91   0.057    -.0208025     .000287
                   |
       c.Age#c.Age |   .0001242   .0000659     1.88   0.060    -5.03e-06    .0002535
                   |
            Female |    -.02784   .0232797    -1.20   0.232    -.0734951    .0178152
      EverEmployed |   .0183121   .0257369     0.71   0.477    -.0321621    .0687862
      EnoughIncome |  -.0262653    .030191    -0.87   0.384    -.0854746     .032944
SecondaryEducation |   .0264172   .0302539     0.87   0.383    -.0329155    .0857499
             Rural |  -.0337768   .0230767    -1.46   0.143     -.079034    .0114803
             _cons |   .6812015   .1033633     6.59   0.000     .4784896    .8839133
------------------------------------------------------------------------------------

. 
. est store reg4

. 
. reg DetectionAttribute c.Age##c.Age Female EverEmployed EnoughIncome SecondaryEducation Rural, robust

Linear regression                               Number of obs     =      1,993
                                                F(7, 1985)        =       1.74
                                                Prob > F          =     0.0946
                                                R-squared         =     0.0060
                                                Root MSE          =     .49935

------------------------------------------------------------------------------------
                   |               Robust
DetectionAttribute | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
               Age |   .0028197   .0053466     0.53   0.598    -.0076658    .0133052
                   |
       c.Age#c.Age |  -.0000388   .0000653    -0.59   0.552    -.0001669    .0000892
                   |
            Female |    .067613   .0232678     2.91   0.004     .0219812    .1132448
      EverEmployed |  -.0263739   .0256879    -1.03   0.305     -.076752    .0240043
      EnoughIncome |   .0299724   .0301563     0.99   0.320    -.0291689    .0891137
SecondaryEducation |  -.0045685    .030396    -0.15   0.881      -.06418     .055043
             Rural |   .0166364   .0230951     0.72   0.471    -.0286568    .0619297
             _cons |   .4363999   .1035369     4.21   0.000     .2333474    .6394523
------------------------------------------------------------------------------------

. 
. est store reg5

. 
. reg FineAttribute c.Age##c.Age Female EverEmployed EnoughIncome SecondaryEducation Rural, robust

Linear regression                               Number of obs     =      1,993
                                                F(7, 1985)        =       2.44
                                                Prob > F          =     0.0174
                                                R-squared         =     0.0081
                                                Root MSE          =     .49855

------------------------------------------------------------------------------------
                   |               Robust
     FineAttribute | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
               Age |  -.0050657   .0052762    -0.96   0.337    -.0154133    .0052818
                   |
       c.Age#c.Age |   .0000804   .0000643     1.25   0.211    -.0000457    .0002065
                   |
            Female |   .0077221   .0232274     0.33   0.740    -.0378305    .0532748
      EverEmployed |   .0639904   .0256515     2.49   0.013     .0136838     .114297
      EnoughIncome |   .0318106   .0301805     1.05   0.292    -.0273783    .0909994
SecondaryEducation |  -.0141039   .0303473    -0.46   0.642    -.0736199     .045412
             Rural |  -.0322622   .0230221    -1.40   0.161    -.0774122    .0128878
             _cons |   .5287196    .102411     5.16   0.000     .3278753    .7295639
------------------------------------------------------------------------------------

. est store reg6

. 
. reg GenderAttribute c.Age##c.Age Female EverEmployed EnoughIncome SecondaryEducation Rural, robust

Linear regression                               Number of obs     =      1,993
                                                F(7, 1985)        =       0.40
                                                Prob > F          =     0.9026
                                                R-squared         =     0.0014
                                                Root MSE          =     .50048

------------------------------------------------------------------------------------
                   |               Robust
   GenderAttribute | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
               Age |   .0052621   .0055216     0.95   0.341    -.0055667    .0160909
                   |
       c.Age#c.Age |  -.0000657   .0000681    -0.96   0.335    -.0001992    .0000679
                   |
            Female |  -.0184998   .0232988    -0.79   0.427    -.0641924    .0271929
      EverEmployed |   .0096009   .0257758     0.37   0.710    -.0409495    .0601514
      EnoughIncome |  -.0046041   .0301846    -0.15   0.879     -.063801    .0545928
SecondaryEducation |   -.020223   .0304114    -0.66   0.506    -.0798646    .0394186
             Rural |   .0139237   .0231007     0.60   0.547    -.0313804    .0592278
             _cons |   .4326825   .1059712     4.08   0.000     .2248562    .6405089
------------------------------------------------------------------------------------

. 
. est store reg7

. 
. reg EarningsAttribute c.Age##c.Age Female EverEmployed EnoughIncome SecondaryEducation Rural, robust

Linear regression                               Number of obs     =      1,993
                                                F(7, 1985)        =       1.54
                                                Prob > F          =     0.1506
                                                R-squared         =     0.0053
                                                Root MSE          =     .49938

------------------------------------------------------------------------------------
                   |               Robust
 EarningsAttribute | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------+----------------------------------------------------------------
               Age |   .0038986   .0055184     0.71   0.480    -.0069239    .0147211
                   |
       c.Age#c.Age |  -.0000514    .000068    -0.75   0.450    -.0001848    .0000821
                   |
            Female |   .0300283   .0232833     1.29   0.197     -.015634    .0756906
      EverEmployed |   .0340902   .0257053     1.33   0.185     -.016322    .0845024
      EnoughIncome |  -.0296758   .0301484    -0.98   0.325    -.0888017    .0294501
SecondaryEducation |  -.0497014   .0301282    -1.65   0.099    -.1087876    .0093848
             Rural |   .0213146   .0230485     0.92   0.355    -.0238871    .0665164
             _cons |   .4486182   .1059343     4.23   0.000      .240864    .6563723
------------------------------------------------------------------------------------

. 
. est store reg8

. 
. *Combining all regressions into one table
. esttab reg1 reg2 reg3 reg4 reg5 reg6 reg7 reg8 using covbalance.tex, se label replace booktabs ///
> alignment (D{.}{.}{-1}) ///
> title( Factor and Respondent Characteristics Balance\label{Table:covbalance})
(output written to covbalance.tex)

. 
. 
. *----------------------------Table A7 
. 
. * We start by creating the mean centered control variables, which are later used in the analysis below. 
. 
. * Centering Age
. egen mean_Age = mean(Age)

. gen meancage = Age - mean_Age

. 
. * Centering Female
. egen mean_Fem = mean(Female)

. gen meancfem = Female - mean_Fem

. 
. * Centering EverEmployed
. egen mean_EverEmployed = mean(EverEmployed)

. gen meancempl = EverEmployed - mean_EverEmployed

. 
. * Centering EnoughIncome
. egen mean_EnoughIncome = mean(EnoughIncome)

. gen meanceninc = EnoughIncome - mean_EnoughIncome
(87 missing values generated)

. 
. * Centering Education 
. egen mean_SecondaryEducation=mean( SecondaryEducation)

. gen meancsedu= SecondaryEducation-mean_SecondaryEducation

. 
. * Rural 
. egen mean_Rural=mean(Rural)

. gen meancrural= Rural-mean_Rural

. 
. *--Estimation of main effects with mean centered covariates
. 
. 
. reg PayTax (ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute iib0.DetectionAttribute 
> ib0.FineAttribute ib0.GenderAttribute ib0.EarningsAttribute)##(c.meancage c.meancfem c.meancempl c.meanceninc c.meancsedu c.meancrural) , robust 

Linear regression                               Number of obs     =      1,943
                                                F(62, 1880)       =       1.72
                                                Prob > F          =     0.0005
                                                R-squared         =     0.0481
                                                Root MSE          =      .4862

----------------------------------------------------------------------------------------------------------
                                         |               Robust
                                  PayTax | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------------------------+----------------------------------------------------------------
             LocalContributionsAttribute |
                         Large Donation  |  -.0497041   .0222874    -2.23   0.026    -.0934147   -.0059935
                                         |
               ServiceProvisionAttribute |
                            Good Access  |   .0452607   .0225643     2.01   0.045     .0010071    .0895144
                                         |
                       FairnessAttribute |
                                Equally  |   .0325108   .0223513     1.45   0.146    -.0113251    .0763467
                                         |
                   TransparencyAttribute |
                               A Lot Of  |   .0072848   .0225815     0.32   0.747    -.0370026    .0515722
                                         |
                      DetectionAttribute |
                                 Likely  |  -.0522471   .0223739    -2.34   0.020    -.0961275   -.0083668
                                         |
                           FineAttribute |
                             Large Fine  |  -.0121558   .0224945    -0.54   0.589    -.0562725     .031961
                                         |
                         GenderAttribute |
                                 Female  |  -.0389255   .0222455    -1.75   0.080     -.082554    .0047029
                                         |
                       EarningsAttribute |
                            120 000 KSH  |   .1094778   .0224644     4.87   0.000     .0654201    .1535355
                                meancage |  -.0010037   .0035396    -0.28   0.777    -.0079457    .0059383
                                meancfem |  -.0829482    .070184    -1.18   0.237    -.2205949    .0546984
                               meancempl |   .0157693   .0763916     0.21   0.836    -.1340519    .1655906
                              meanceninc |    .089118   .0924428     0.96   0.335    -.0921834    .2704193
                               meancsedu |   .1225314   .0985975     1.24   0.214    -.0708405    .3159034
                              meancrural |   .0452265   .0713972     0.63   0.527    -.0947995    .1852525
                                         |
  LocalContributionsAttribute#c.meancage |
                         Large Donation  |   .0006799   .0022517     0.30   0.763    -.0037362     .005096
                                         |
  LocalContributionsAttribute#c.meancfem |
                         Large Donation  |  -.0015824   .0462318    -0.03   0.973    -.0922535    .0890886
                                         |
 LocalContributionsAttribute#c.meancempl |
                         Large Donation  |  -.0134391   .0511202    -0.26   0.793    -.1136973    .0868192
                                         |
LocalContributionsAttribute#c.meanceninc |
                         Large Donation  |  -.0911128   .0590809    -1.54   0.123    -.2069839    .0247583
                                         |
 LocalContributionsAttribute#c.meancsedu |
                         Large Donation  |  -.0562912   .0623366    -0.90   0.367    -.1785475     .065965
                                         |
LocalContributionsAttribute#c.meancrural |
                         Large Donation  |  -.0266432   .0460351    -0.58   0.563    -.1169284    .0636421
                                         |
    ServiceProvisionAttribute#c.meancage |
                            Good Access  |  -.0035176   .0022974    -1.53   0.126    -.0080233    .0009881
                                         |
    ServiceProvisionAttribute#c.meancfem |
                            Good Access  |   -.000843   .0464438    -0.02   0.986    -.0919298    .0902438
                                         |
   ServiceProvisionAttribute#c.meancempl |
                            Good Access  |  -.0227622   .0514409    -0.44   0.658    -.1236496    .0781251
                                         |
  ServiceProvisionAttribute#c.meanceninc |
                            Good Access  |   .0912688   .0593703     1.54   0.124    -.0251698    .2077074
                                         |
   ServiceProvisionAttribute#c.meancsedu |
                            Good Access  |  -.0010728   .0623218    -0.02   0.986       -.1233    .1211544
                                         |
  ServiceProvisionAttribute#c.meancrural |
                            Good Access  |  -.0441943    .046273    -0.96   0.340    -.1349461    .0465575
                                         |
            FairnessAttribute#c.meancage |
                                Equally  |    .002825   .0022639     1.25   0.212     -.001615     .007265
                                         |
            FairnessAttribute#c.meancfem |
                                Equally  |  -.0060861    .046259    -0.13   0.895    -.0968105    .0846384
                                         |
           FairnessAttribute#c.meancempl |
                                Equally  |  -.0151439    .051174    -0.30   0.767    -.1155076    .0852199
                                         |
          FairnessAttribute#c.meanceninc |
                                Equally  |  -.0422731   .0594454    -0.71   0.477     -.158859    .0743128
                                         |
           FairnessAttribute#c.meancsedu |
                                Equally  |  -.0008559   .0621434    -0.01   0.989    -.1227332    .1210215
                                         |
          FairnessAttribute#c.meancrural |
                                Equally  |   -.026028   .0459011    -0.57   0.571    -.1160504    .0639944
                                         |
        TransparencyAttribute#c.meancage |
                               A Lot Of  |    .003472   .0023244     1.49   0.135    -.0010867    .0080307
                                         |
        TransparencyAttribute#c.meancfem |
                               A Lot Of  |  -.0111964    .046089    -0.24   0.808    -.1015874    .0791946
                                         |
       TransparencyAttribute#c.meancempl |
                               A Lot Of  |  -.1359929     .05108    -2.66   0.008    -.2361723   -.0358135
                                         |
      TransparencyAttribute#c.meanceninc |
                               A Lot Of  |   .0040945   .0594527     0.07   0.945    -.1125057    .1206947
                                         |
       TransparencyAttribute#c.meancsedu |
                               A Lot Of  |  -.0038786   .0629738    -0.06   0.951    -.1273844    .1196272
                                         |
      TransparencyAttribute#c.meancrural |
                               A Lot Of  |  -.0274831   .0458288    -0.60   0.549    -.1173637    .0623975
                                         |
           DetectionAttribute#c.meancage |
                                 Likely  |  -.0003294   .0022517    -0.15   0.884    -.0047454    .0040867
                                         |
           DetectionAttribute#c.meancfem |
                                 Likely  |   .0411768   .0464409     0.89   0.375    -.0499042    .1322579
                                         |
          DetectionAttribute#c.meancempl |
                                 Likely  |   .0836663   .0509596     1.64   0.101    -.0162769    .1836095
                                         |
         DetectionAttribute#c.meanceninc |
                                 Likely  |  -.0167343   .0591868    -0.28   0.777    -.1328131    .0993446
                                         |
          DetectionAttribute#c.meancsedu |
                                 Likely  |  -.0975405   .0624484    -1.56   0.118    -.2200159    .0249349
                                         |
         DetectionAttribute#c.meancrural |
                                 Likely  |  -.0025324   .0460056    -0.06   0.956    -.0927597     .087695
                                         |
                FineAttribute#c.meancage |
                             Large Fine  |   .0021107   .0022919     0.92   0.357    -.0023843    .0066058
                                         |
                FineAttribute#c.meancfem |
                             Large Fine  |   .0446629   .0462034     0.97   0.334    -.0459526    .1352783
                                         |
               FineAttribute#c.meancempl |
                             Large Fine  |  -.0104864   .0510654    -0.21   0.837    -.1106373    .0896645
                                         |
              FineAttribute#c.meanceninc |
                             Large Fine  |  -.0481772   .0590349    -0.82   0.415    -.1639579    .0676035
                                         |
               FineAttribute#c.meancsedu |
                             Large Fine  |  -.0294651    .062409    -0.47   0.637    -.1518633    .0929332
                                         |
              FineAttribute#c.meancrural |
                             Large Fine  |  -.0576264   .0458293    -1.26   0.209    -.1475079    .0322552
                                         |
              GenderAttribute#c.meancage |
                                 Female  |   .0002998    .002264     0.13   0.895    -.0041404    .0047399
                                         |
              GenderAttribute#c.meancfem |
                                 Female  |   .0029899   .0461376     0.06   0.948    -.0874963    .0934762
                                         |
             GenderAttribute#c.meancempl |
                                 Female  |  -.0218869   .0510578    -0.43   0.668    -.1220229     .078249
                                         |
            GenderAttribute#c.meanceninc |
                                 Female  |  -.0066441   .0586757    -0.11   0.910    -.1217204    .1084323
                                         |
             GenderAttribute#c.meancsedu |
                                 Female  |   .0218212   .0631221     0.35   0.730    -.1019756    .1456179
                                         |
            GenderAttribute#c.meancrural |
                                 Female  |   .0059517   .0457231     0.13   0.896    -.0837217    .0956252
                                         |
            EarningsAttribute#c.meancage |
                            120 000 KSH  |  -.0000692   .0022802    -0.03   0.976    -.0045412    .0044028
                                         |
            EarningsAttribute#c.meancfem |
                            120 000 KSH  |  -.0145324   .0460659    -0.32   0.752    -.1048781    .0758132
                                         |
           EarningsAttribute#c.meancempl |
                            120 000 KSH  |   .0537194   .0510505     1.05   0.293    -.0464022    .1538409
                                         |
          EarningsAttribute#c.meanceninc |
                            120 000 KSH  |  -.0269397   .0589209    -0.46   0.648     -.142497    .0886176
                                         |
           EarningsAttribute#c.meancsedu |
                            120 000 KSH  |  -.0097371   .0633063    -0.15   0.878    -.1338952     .114421
                                         |
          EarningsAttribute#c.meancrural |
                            120 000 KSH  |   .0172122   .0458319     0.38   0.707    -.0726746    .1070989
                                         |
                                   _cons |   .5760873    .034526    16.69   0.000     .5083741    .6438005
----------------------------------------------------------------------------------------------------------

. est store reg1 

. 
. reg Justifiable (ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute iib0.DetectionAttri
> bute ib0.FineAttribute ib0.GenderAttribute ib0.EarningsAttribute)##(c.meancage c.meancfem c.meancempl c.meanceninc c.meancsedu c.meancrural) , rob
> ust 

Linear regression                               Number of obs     =      1,910
                                                F(62, 1847)       =       1.36
                                                Prob > F          =     0.0329
                                                R-squared         =     0.0390
                                                Root MSE          =     .49732

----------------------------------------------------------------------------------------------------------
                                         |               Robust
                             Justifiable | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------------------------+----------------------------------------------------------------
             LocalContributionsAttribute |
                         Large Donation  |   .0064716   .0231713     0.28   0.780    -.0389731    .0519163
                                         |
               ServiceProvisionAttribute |
                            Good Access  |    .026967   .0232677     1.16   0.247    -.0186669    .0726008
                                         |
                       FairnessAttribute |
                                Equally  |  -.0067008   .0230868    -0.29   0.772    -.0519798    .0385782
                                         |
                   TransparencyAttribute |
                               A Lot Of  |   .0017125   .0232069     0.07   0.941    -.0438021    .0472271
                                         |
                      DetectionAttribute |
                                 Likely  |  -.0178829   .0231526    -0.77   0.440     -.063291    .0275252
                                         |
                           FineAttribute |
                             Large Fine  |  -.0213553   .0231249    -0.92   0.356     -.066709    .0239984
                                         |
                         GenderAttribute |
                                 Female  |   .0136162   .0230639     0.59   0.555    -.0316179    .0588503
                                         |
                       EarningsAttribute |
                            120 000 KSH  |   -.013999   .0232567    -0.60   0.547    -.0596111    .0316131
                                meancage |   .0018707   .0035146     0.53   0.595    -.0050224    .0087637
                                meancfem |   .0969851   .0721427     1.34   0.179    -.0445047    .2384748
                               meancempl |   .1223206   .0778231     1.57   0.116    -.0303099    .2749511
                              meanceninc |   .0102499   .0956567     0.11   0.915    -.1773567    .1978566
                               meancsedu |  -.0371392   .1029353    -0.36   0.718    -.2390209    .1647425
                              meancrural |   .0240696   .0733429     0.33   0.743    -.1197741    .1679134
                                         |
  LocalContributionsAttribute#c.meancage |
                         Large Donation  |  -.0015429   .0022935    -0.67   0.501    -.0060411    .0029554
                                         |
  LocalContributionsAttribute#c.meancfem |
                         Large Donation  |  -.0360398   .0475071    -0.76   0.448    -.1292131    .0571335
                                         |
 LocalContributionsAttribute#c.meancempl |
                         Large Donation  |  -.0859167   .0520294    -1.65   0.099    -.1879594     .016126
                                         |
LocalContributionsAttribute#c.meanceninc |
                         Large Donation  |  -.0566685    .061492    -0.92   0.357    -.1772698    .0639327
                                         |
 LocalContributionsAttribute#c.meancsedu |
                         Large Donation  |  -.0300923   .0635698    -0.47   0.636    -.1547685     .094584
                                         |
LocalContributionsAttribute#c.meancrural |
                         Large Donation  |  -.0539734    .047497    -1.14   0.256    -.1471269    .0391801
                                         |
    ServiceProvisionAttribute#c.meancage |
                            Good Access  |   .0021112   .0023265     0.91   0.364    -.0024517     .006674
                                         |
    ServiceProvisionAttribute#c.meancfem |
                            Good Access  |  -.0352993   .0476306    -0.74   0.459    -.1287149    .0581162
                                         |
   ServiceProvisionAttribute#c.meancempl |
                            Good Access  |  -.1285902   .0519019    -2.48   0.013    -.2303827   -.0267977
                                         |
  ServiceProvisionAttribute#c.meanceninc |
                            Good Access  |   .1119784     .06165     1.82   0.069    -.0089326    .2328894
                                         |
   ServiceProvisionAttribute#c.meancsedu |
                            Good Access  |   .0364299   .0642554     0.57   0.571     -.089591    .1624508
                                         |
  ServiceProvisionAttribute#c.meancrural |
                            Good Access  |  -.0755718   .0474822    -1.59   0.112    -.1686962    .0175525
                                         |
            FairnessAttribute#c.meancage |
                                Equally  |  -.0019512   .0023001    -0.85   0.396    -.0064622    .0025598
                                         |
            FairnessAttribute#c.meancfem |
                                Equally  |  -.0942094   .0476016    -1.98   0.048     -.187568   -.0008509
                                         |
           FairnessAttribute#c.meancempl |
                                Equally  |   .0736519   .0517347     1.42   0.155    -.0278128    .1751165
                                         |
          FairnessAttribute#c.meanceninc |
                                Equally  |   -.021339   .0619705    -0.34   0.731    -.1428786    .1002006
                                         |
           FairnessAttribute#c.meancsedu |
                                Equally  |  -.1084335   .0645537    -1.68   0.093    -.2350394    .0181725
                                         |
          FairnessAttribute#c.meancrural |
                                Equally  |   .0075195   .0475386     0.16   0.874    -.0857154    .1007545
                                         |
        TransparencyAttribute#c.meancage |
                               A Lot Of  |   -.004505   .0023326    -1.93   0.054    -.0090799    .0000699
                                         |
        TransparencyAttribute#c.meancfem |
                               A Lot Of  |  -.0701509   .0477296    -1.47   0.142    -.1637606    .0234588
                                         |
       TransparencyAttribute#c.meancempl |
                               A Lot Of  |  -.0102776    .051861    -0.20   0.843      -.11199    .0914347
                                         |
      TransparencyAttribute#c.meanceninc |
                               A Lot Of  |  -.0321409   .0614836    -0.52   0.601    -.1527255    .0884437
                                         |
       TransparencyAttribute#c.meancsedu |
                               A Lot Of  |   .0116559   .0644844     0.18   0.857    -.1148141     .138126
                                         |
      TransparencyAttribute#c.meancrural |
                               A Lot Of  |    .031955   .0475912     0.67   0.502    -.0613832    .1252932
                                         |
           DetectionAttribute#c.meancage |
                                 Likely  |   .0023807    .002301     1.03   0.301     -.002132    .0068935
                                         |
           DetectionAttribute#c.meancfem |
                                 Likely  |   .0873755   .0477947     1.83   0.068    -.0063619    .1811128
                                         |
          DetectionAttribute#c.meancempl |
                                 Likely  |  -.0360863   .0517879    -0.70   0.486    -.1376553    .0654828
                                         |
         DetectionAttribute#c.meanceninc |
                                 Likely  |  -.0268101   .0610699    -0.44   0.661    -.1465834    .0929631
                                         |
          DetectionAttribute#c.meancsedu |
                                 Likely  |   .1344779   .0644282     2.09   0.037     .0081182    .2608376
                                         |
         DetectionAttribute#c.meancrural |
                                 Likely  |  -.1018703   .0475585    -2.14   0.032    -.1951445   -.0085962
                                         |
                FineAttribute#c.meancage |
                             Large Fine  |   -.003143   .0023087    -1.36   0.174     -.007671    .0013849
                                         |
                FineAttribute#c.meancfem |
                             Large Fine  |  -.0470642   .0476329    -0.99   0.323    -.1404842    .0463559
                                         |
               FineAttribute#c.meancempl |
                             Large Fine  |  -.0123304   .0519227    -0.24   0.812    -.1141638     .089503
                                         |
              FineAttribute#c.meanceninc |
                             Large Fine  |     .00562   .0612668     0.09   0.927    -.1145395    .1257795
                                         |
               FineAttribute#c.meancsedu |
                             Large Fine  |  -.1047835   .0634729    -1.65   0.099    -.2292697    .0197027
                                         |
              FineAttribute#c.meancrural |
                             Large Fine  |   .0032634   .0474954     0.07   0.945    -.0898868    .0964136
                                         |
              GenderAttribute#c.meancage |
                                 Female  |  -.0017817   .0022986    -0.78   0.438    -.0062899    .0027264
                                         |
              GenderAttribute#c.meancfem |
                                 Female  |  -.0188799   .0475472    -0.40   0.691    -.1121318     .074372
                                         |
             GenderAttribute#c.meancempl |
                                 Female  |    .140258   .0516686     2.71   0.007     .0389231     .241593
                                         |
            GenderAttribute#c.meanceninc |
                                 Female  |  -.0934375   .0610857    -1.53   0.126    -.2132419    .0263668
                                         |
             GenderAttribute#c.meancsedu |
                                 Female  |  -.0261878   .0644503    -0.41   0.685    -.1525909    .1002152
                                         |
            GenderAttribute#c.meancrural |
                                 Female  |   .0407005   .0474111     0.86   0.391    -.0522846    .1336856
                                         |
            EarningsAttribute#c.meancage |
                            120 000 KSH  |   .0024927   .0023175     1.08   0.282    -.0020525    .0070379
                                         |
            EarningsAttribute#c.meancfem |
                            120 000 KSH  |   .0036212   .0474778     0.08   0.939    -.0894946    .0967369
                                         |
           EarningsAttribute#c.meancempl |
                            120 000 KSH  |  -.0780808   .0518559    -1.51   0.132    -.1797831    .0236216
                                         |
          EarningsAttribute#c.meanceninc |
                            120 000 KSH  |   .0104126   .0612335     0.17   0.865    -.1096816    .1305068
                                         |
           EarningsAttribute#c.meancsedu |
                            120 000 KSH  |   .0478365   .0643701     0.74   0.457    -.0784093    .1740824
                                         |
          EarningsAttribute#c.meancrural |
                            120 000 KSH  |   .0411542   .0475057     0.87   0.386    -.0520163    .1343248
                                         |
                                   _cons |   .4714905   .0355444    13.26   0.000     .4017791     .541202
----------------------------------------------------------------------------------------------------------

. est store reg2 

. 
. reg TrustGov (ib0.LocalContributionsAttribute ib0.ServiceProvisionAttribute ib0.FairnessAttribute ib0.TransparencyAttribute iib0.DetectionAttribut
> e ib0.FineAttribute ib0.GenderAttribute ib0.EarningsAttribute)##(c.meancage c.meancfem c.meancempl c.meanceninc c.meancsedu c.meancrural) , robust
>  

Linear regression                               Number of obs     =      1,915
                                                F(62, 1852)       =       1.21
                                                Prob > F          =     0.1288
                                                R-squared         =     0.0353
                                                Root MSE          =     .49611

----------------------------------------------------------------------------------------------------------
                                         |               Robust
                                TrustGov | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-----------------------------------------+----------------------------------------------------------------
             LocalContributionsAttribute |
                         Large Donation  |    -.02952   .0230644    -1.28   0.201     -.074755     .015715
                                         |
               ServiceProvisionAttribute |
                            Good Access  |   .0416058   .0231805     1.79   0.073     -.003857    .0870685
                                         |
                       FairnessAttribute |
                                Equally  |   .0267123    .023011     1.16   0.246    -.0184179    .0718424
                                         |
                   TransparencyAttribute |
                               A Lot Of  |   .0233597    .023172     1.01   0.314    -.0220862    .0688056
                                         |
                      DetectionAttribute |
                                 Likely  |  -.0157002   .0230372    -0.68   0.496    -.0608819    .0294814
                                         |
                           FineAttribute |
                             Large Fine  |   .0440296   .0230525     1.91   0.056    -.0011821    .0892413
                                         |
                         GenderAttribute |
                                 Female  |  -.0553876   .0229289    -2.42   0.016    -.1003568   -.0104184
                                         |
                       EarningsAttribute |
                            120 000 KSH  |   .0122961   .0231359     0.53   0.595    -.0330791    .0576712
                                meancage |   .0015335   .0035403     0.43   0.665    -.0054099    .0084768
                                meancfem |  -.0561561   .0717245    -0.78   0.434    -.1968254    .0845132
                               meancempl |   .0521662   .0780103     0.67   0.504    -.1008312    .2051636
                              meanceninc |  -.1838887   .0942993    -1.95   0.051    -.3688329    .0010554
                               meancsedu |   .1394969   .0987107     1.41   0.158    -.0540991    .3330929
                              meancrural |   .0668794   .0731755     0.91   0.361    -.0766357    .2103945
                                         |
  LocalContributionsAttribute#c.meancage |
                         Large Donation  |  -.0017761   .0023178    -0.77   0.444    -.0063218    .0027696
                                         |
  LocalContributionsAttribute#c.meancfem |
                         Large Donation  |   .0467881   .0472305     0.99   0.322    -.0458424    .1394187
                                         |
 LocalContributionsAttribute#c.meancempl |
                         Large Donation  |  -.0006672   .0523484    -0.01   0.990    -.1033352    .1020009
                                         |
LocalContributionsAttribute#c.meanceninc |
                         Large Donation  |  -.0184709   .0602364    -0.31   0.759    -.1366092    .0996675
                                         |
 LocalContributionsAttribute#c.meancsedu |
                         Large Donation  |   -.050167   .0628406    -0.80   0.425    -.1734128    .0730789
                                         |
LocalContributionsAttribute#c.meancrural |
                         Large Donation  |  -.0037207   .0476871    -0.08   0.938    -.0972468    .0898055
                                         |
    ServiceProvisionAttribute#c.meancage |
                            Good Access  |  -.0019496   .0023229    -0.84   0.401    -.0065053    .0026061
                                         |
    ServiceProvisionAttribute#c.meancfem |
                            Good Access  |  -.0099184   .0472785    -0.21   0.834    -.1026433    .0828064
                                         |
   ServiceProvisionAttribute#c.meancempl |
                            Good Access  |   .0498545   .0522604     0.95   0.340    -.0526411      .15235
                                         |
  ServiceProvisionAttribute#c.meanceninc |
                            Good Access  |   .1617419   .0605986     2.67   0.008     .0428932    .2805906
                                         |
   ServiceProvisionAttribute#c.meancsedu |
                            Good Access  |  -.1048905   .0630607    -1.66   0.096     -.228568     .018787
                                         |
  ServiceProvisionAttribute#c.meancrural |
                            Good Access  |  -.0208167   .0475802    -0.44   0.662    -.1141331    .0724996
                                         |
            FairnessAttribute#c.meancage |
                                Equally  |   .0012756   .0023079     0.55   0.581    -.0032507     .005802
                                         |
            FairnessAttribute#c.meancfem |
                                Equally  |  -.0715238   .0472307    -1.51   0.130    -.1641548    .0211072
                                         |
           FairnessAttribute#c.meancempl |
                                Equally  |   .0319269   .0518975     0.62   0.539    -.0698567    .1337106
                                         |
          FairnessAttribute#c.meanceninc |
                                Equally  |  -.0083112   .0608664    -0.14   0.891    -.1276853    .1110628
                                         |
           FairnessAttribute#c.meancsedu |
                                Equally  |  -.1128976   .0632624    -1.78   0.074    -.2369707    .0111755
                                         |
          FairnessAttribute#c.meancrural |
                                Equally  |   -.047888   .0476298    -1.01   0.315    -.1413017    .0455258
                                         |
        TransparencyAttribute#c.meancage |
                               A Lot Of  |   .0000305   .0023444     0.01   0.990    -.0045674    .0046283
                                         |
        TransparencyAttribute#c.meancfem |
                               A Lot Of  |  -.0609815   .0473781    -1.29   0.198    -.1539017    .0319387
                                         |
       TransparencyAttribute#c.meancempl |
                               A Lot Of  |  -.0382443   .0520268    -0.74   0.462    -.1402816    .0637929
                                         |
      TransparencyAttribute#c.meanceninc |
                               A Lot Of  |   .0469654   .0606145     0.77   0.439    -.0719146    .1658454
                                         |
       TransparencyAttribute#c.meancsedu |
                               A Lot Of  |   .0310211   .0634869     0.49   0.625    -.0934923    .1555345
                                         |
      TransparencyAttribute#c.meancrural |
                               A Lot Of  |   .0312664   .0475437     0.66   0.511    -.0619786    .1245113
                                         |
           DetectionAttribute#c.meancage |
                                 Likely  |  -.0006454     .00231    -0.28   0.780    -.0051757     .003885
                                         |
           DetectionAttribute#c.meancfem |
                                 Likely  |  -.0010666    .047437    -0.02   0.982    -.0941022     .091969
                                         |
          DetectionAttribute#c.meancempl |
                                 Likely  |   -.066744   .0518658    -1.29   0.198    -.1684656    .0349777
                                         |
         DetectionAttribute#c.meanceninc |
                                 Likely  |   .0571368   .0603097     0.95   0.344    -.0611454    .1754191
                                         |
          DetectionAttribute#c.meancsedu |
                                 Likely  |  -.0732214   .0635627    -1.15   0.249    -.1978835    .0514408
                                         |
         DetectionAttribute#c.meancrural |
                                 Likely  |   .0012268   .0474696     0.03   0.979    -.0918726    .0943263
                                         |
                FineAttribute#c.meancage |
                             Large Fine  |   .0020717   .0023111     0.90   0.370    -.0024609    .0066043
                                         |
                FineAttribute#c.meancfem |
                             Large Fine  |    .110912   .0471967     2.35   0.019     .0183476    .2034764
                                         |
               FineAttribute#c.meancempl |
                             Large Fine  |   .0093229   .0521388     0.18   0.858    -.0929341    .1115799
                                         |
              FineAttribute#c.meanceninc |
                             Large Fine  |   .0087189   .0600936     0.15   0.885    -.1091395    .1265772
                                         |
               FineAttribute#c.meancsedu |
                             Large Fine  |  -.0045202   .0623092    -0.07   0.942    -.1267239    .1176835
                                         |
              FineAttribute#c.meancrural |
                             Large Fine  |  -.0806408   .0472793    -1.71   0.088    -.1733671    .0120856
                                         |
              GenderAttribute#c.meancage |
                                 Female  |  -.0024514   .0023079    -1.06   0.288    -.0069778     .002075
                                         |
              GenderAttribute#c.meancfem |
                                 Female  |  -.0185486   .0473696    -0.39   0.695     -.111452    .0743548
                                         |
             GenderAttribute#c.meancempl |
                                 Female  |   .0275241   .0519213     0.53   0.596    -.0743064    .1293546
                                         |
            GenderAttribute#c.meanceninc |
                                 Female  |   .0344994     .06029     0.57   0.567    -.0837441    .1527429
                                         |
             GenderAttribute#c.meancsedu |
                                 Female  |  -.0165398   .0633201    -0.26   0.794    -.1407262    .1076465
                                         |
            GenderAttribute#c.meancrural |
                                 Female  |  -.0077635   .0473803    -0.16   0.870    -.1006878    .0851609
                                         |
            EarningsAttribute#c.meancage |
                            120 000 KSH  |   .0017008   .0023432     0.73   0.468    -.0028947    .0062963
                                         |
            EarningsAttribute#c.meancfem |
                            120 000 KSH  |   .0536345   .0472543     1.14   0.257    -.0390427    .1463117
                                         |
           EarningsAttribute#c.meancempl |
                            120 000 KSH  |   -.046478   .0522406    -0.89   0.374    -.1489346    .0559785
                                         |
          EarningsAttribute#c.meanceninc |
                            120 000 KSH  |   -.023844   .0602079    -0.40   0.692    -.1419265    .0942385
                                         |
           EarningsAttribute#c.meancsedu |
                            120 000 KSH  |   .0099208   .0635833     0.16   0.876    -.1147816    .1346233
                                         |
          EarningsAttribute#c.meancrural |
                            120 000 KSH  |   .0076585   .0473603     0.16   0.872    -.0852268    .1005437
                                         |
                                   _cons |   .4188933   .0352705    11.88   0.000     .3497191    .4880675
----------------------------------------------------------------------------------------------------------

. est store reg3

. 
. *Combining all regressions into one table
. esttab reg1 reg2 reg3  using mainmeancent.tex, se label replace booktabs ///
> alignment (D{.}{.}{-1}) ///
> title(Main Effects With Mean Centered Covariates \label{Table:mainmeancent})
(output written to mainmeancent.tex)

. 
. *----------------------------Table A8 
. 
. reg PayTax ib0.LocalContributionsAttribute  ib0.ServiceProvisionAttribute ServiceProvisionAttribute#LocalContributionsAttribute ib0.FairnessAttrib
> ute ib0.TransparencyAttribute FairnessAttribute#TransparencyAttribute ib0.DetectionAttribute ib0.FineAttribute DetectionAttribute#FineAttribute ib
> 0.GenderAttribute ib0.EarningsAttribute, robust 

Linear regression                               Number of obs     =      2,026
                                                F(11, 2014)       =       4.68
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0241
                                                Root MSE          =     .48614

-----------------------------------------------------------------------------------------------------------------------
                                                      |               Robust
                                               PayTax | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------------------------------------+----------------------------------------------------------------
                          LocalContributionsAttribute |
                                      Large Donation  |  -.0277531    .031723    -0.87   0.382    -.0899665    .0344603
                                                      |
                            ServiceProvisionAttribute |
                                         Good Access  |   .0592907   .0298964     1.98   0.047     .0006595    .1179219
                                                      |
ServiceProvisionAttribute#LocalContributionsAttribute |
                          Good Access#Large Donation  |  -.0212588   .0433999    -0.49   0.624    -.1063723    .0638547
                                                      |
                                    FairnessAttribute |
                                             Equally  |   .0594817   .0303743     1.96   0.050    -.0000866    .1190501
                                                      |
                                TransparencyAttribute |
                                            A Lot Of  |   .0313284   .0307363     1.02   0.308    -.0289499    .0916067
                                                      |
              FairnessAttribute#TransparencyAttribute |
                                    Equally#A Lot Of  |   -.051376   .0432854    -1.19   0.235    -.1362648    .0335129
                                                      |
                                   DetectionAttribute |
                                              Likely  |  -.0703278   .0298813    -2.35   0.019    -.1289293   -.0117262
                                                      |
                                        FineAttribute |
                                          Large Fine  |  -.0300824   .0306624    -0.98   0.327    -.0902158    .0300509
                                                      |
                     DetectionAttribute#FineAttribute |
                                   Likely#Large Fine  |   .0244098   .0433705     0.56   0.574     -.060646    .1094656
                                                      |
                                      GenderAttribute |
                                              Female  |  -.0404931   .0216847    -1.87   0.062    -.0830198    .0020337
                                                      |
                                    EarningsAttribute |
                                         120 000 KSH  |    .106345   .0216834     4.90   0.000     .0638208    .1488691
                                                _cons |   .5612311    .038187    14.70   0.000      .486341    .6361212
-----------------------------------------------------------------------------------------------------------------------

. 
. est store reg1 

. 
. reg Justifiable ib0.LocalContributionsAttribute  ib0.ServiceProvisionAttribute ServiceProvisionAttribute#LocalContributionsAttribute ib0.FairnessA
> ttribute ib0.TransparencyAttribute FairnessAttribute#TransparencyAttribute ib0.DetectionAttribute ib0.FineAttribute DetectionAttribute#FineAttribu
> te ib0.GenderAttribute ib0.EarningsAttribute, robust 

Linear regression                               Number of obs     =      1,990
                                                F(11, 1978)       =       0.85
                                                Prob > F          =     0.5914
                                                R-squared         =     0.0047
                                                Root MSE          =     .49921

-----------------------------------------------------------------------------------------------------------------------
                                                      |               Robust
                                          Justifiable | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------------------------------------+----------------------------------------------------------------
                          LocalContributionsAttribute |
                                      Large Donation  |   .0169379   .0324171     0.52   0.601    -.0466374    .0805132
                                                      |
                            ServiceProvisionAttribute |
                                         Good Access  |   .0222454   .0311575     0.71   0.475    -.0388597    .0833504
                                                      |
ServiceProvisionAttribute#LocalContributionsAttribute |
                          Good Access#Large Donation  |  -.0044184   .0448776    -0.10   0.922    -.0924308     .083594
                                                      |
                                    FairnessAttribute |
                                             Equally  |  -.0417393   .0312872    -1.33   0.182    -.1030986    .0196201
                                                      |
                                TransparencyAttribute |
                                            A Lot Of  |  -.0306657   .0317083    -0.97   0.334    -.0928509    .0315194
                                                      |
              FairnessAttribute#TransparencyAttribute |
                                    Equally#A Lot Of  |   .0690169   .0448382     1.54   0.124    -.0189183     .156952
                                                      |
                                   DetectionAttribute |
                                              Likely  |  -.0477992   .0312246    -1.53   0.126    -.1090357    .0134373
                                                      |
                                        FineAttribute |
                                          Large Fine  |  -.0477611   .0321269    -1.49   0.137    -.1107672    .0152451
                                                      |
                     DetectionAttribute#FineAttribute |
                                   Likely#Large Fine  |   .0649336   .0448863     1.45   0.148    -.0230959    .1529631
                                                      |
                                      GenderAttribute |
                                              Female  |   .0259931   .0224739     1.16   0.248     -.018082    .0700681
                                                      |
                                    EarningsAttribute |
                                         120 000 KSH  |  -.0276095   .0224581    -1.23   0.219    -.0716536    .0164345
                                                _cons |    .499231   .0394253    12.66   0.000     .4219115    .5765505
-----------------------------------------------------------------------------------------------------------------------

. 
. est store reg2

. 
. reg TrustGov ib0.LocalContributionsAttribute  ib0.ServiceProvisionAttribute ServiceProvisionAttribute#LocalContributionsAttribute ib0.FairnessAttr
> ibute ib0.TransparencyAttribute FairnessAttribute#TransparencyAttribute ib0.DetectionAttribute ib0.FineAttribute DetectionAttribute#FineAttribute 
> ib0.GenderAttribute ib0.EarningsAttribute, robust 

Linear regression                               Number of obs     =      1,995
                                                F(11, 1983)       =       1.84
                                                Prob > F          =     0.0422
                                                R-squared         =     0.0100
                                                Root MSE          =     .49565

-----------------------------------------------------------------------------------------------------------------------
                                                      |               Robust
                                             TrustGov | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------------------------------------+----------------------------------------------------------------
                          LocalContributionsAttribute |
                                      Large Donation  |  -.0686217   .0320531    -2.14   0.032     -.131483   -.0057604
                                                      |
                            ServiceProvisionAttribute |
                                         Good Access  |   .0078409   .0310158     0.25   0.800     -.052986    .0686679
                                                      |
ServiceProvisionAttribute#LocalContributionsAttribute |
                          Good Access#Large Donation  |   .0769508   .0445405     1.73   0.084    -.0104004     .164302
                                                      |
                                    FairnessAttribute |
                                             Equally  |   .0080748   .0310822     0.26   0.795    -.0528824    .0690321
                                                      |
                                TransparencyAttribute |
                                            A Lot Of  |   .0071328   .0314276     0.23   0.820    -.0545018    .0687674
                                                      |
              FairnessAttribute#TransparencyAttribute |
                                    Equally#A Lot Of  |   .0187278   .0444762     0.42   0.674    -.0684973    .1059528
                                                      |
                                   DetectionAttribute |
                                              Likely  |   .0031592   .0309136     0.10   0.919    -.0574672    .0637857
                                                      |
                                        FineAttribute |
                                          Large Fine  |   .0601074   .0318182     1.89   0.059    -.0022933     .122508
                                                      |
                     DetectionAttribute#FineAttribute |
                                   Likely#Large Fine  |  -.0407256   .0444741    -0.92   0.360    -.1279466    .0464953
                                                      |
                                      GenderAttribute |
                                              Female  |  -.0515608   .0222725    -2.32   0.021    -.0952408   -.0078809
                                                      |
                                    EarningsAttribute |
                                         120 000 KSH  |   .0124824   .0222652     0.56   0.575    -.0311832    .0561479
                                                _cons |    .439509   .0393187    11.18   0.000     .3623987    .5166194
-----------------------------------------------------------------------------------------------------------------------

. est store reg3

. 
. 
. *Combining all regressions into one table
. esttab reg1 reg2 reg3 using interactions.tex, se label replace booktabs ///
> alignment (D{.}{.}{-1}) ///
> title(Factor Interactions \label{Table:interactions})
(output written to interactions.tex)

. 
. *----------------------------Table A9 
. 
. reg PayTax ib0.LocalContributionsAttribute  ib0.ServiceProvisionAttribute ServiceProvisionAttribute#LocalContributionsAttribute ib0.FairnessAttrib
> ute ib0.TransparencyAttribute FairnessAttribute#TransparencyAttribute ib0.DetectionAttribute ib0.FineAttribute DetectionAttribute#FineAttribute ib
> 0.GenderAttribute ib0.EarningsAttribute if EnoughIncome==1, robust 

Linear regression                               Number of obs     =        397
                                                F(11, 385)        =       2.18
                                                Prob > F          =     0.0151
                                                R-squared         =     0.0539
                                                Root MSE          =     .47888

-----------------------------------------------------------------------------------------------------------------------
                                                      |               Robust
                                               PayTax | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------------------------------------+----------------------------------------------------------------
                          LocalContributionsAttribute |
                                      Large Donation  |  -.1548078    .072116    -2.15   0.032    -.2965983   -.0130172
                                                      |
                            ServiceProvisionAttribute |
                                         Good Access  |   .0835253    .067397     1.24   0.216     -.048987    .2160377
                                                      |
ServiceProvisionAttribute#LocalContributionsAttribute |
                          Good Access#Large Donation  |   .0557947   .0970616     0.57   0.566    -.1350425    .2466318
                                                      |
                                    FairnessAttribute |
                                             Equally  |  -.0096837   .0671038    -0.14   0.885    -.1416194     .122252
                                                      |
                                TransparencyAttribute |
                                            A Lot Of  |  -.0244642   .0707414    -0.35   0.730    -.1635519    .1146236
                                                      |
              FairnessAttribute#TransparencyAttribute |
                                    Equally#A Lot Of  |   .0088531   .0972315     0.09   0.927    -.1823181    .2000243
                                                      |
                                   DetectionAttribute |
                                              Likely  |  -.1139982   .0691642    -1.65   0.100    -.2499851    .0219887
                                                      |
                                        FineAttribute |
                                          Large Fine  |  -.1099747   .0694365    -1.58   0.114    -.2464968    .0265474
                                                      |
                     DetectionAttribute#FineAttribute |
                                   Likely#Large Fine  |   .0967623   .0981228     0.99   0.325    -.0961613    .2896859
                                                      |
                                      GenderAttribute |
                                              Female  |  -.0419675   .0487803    -0.86   0.390    -.1378767    .0539417
                                                      |
                                    EarningsAttribute |
                                         120 000 KSH  |   .1004769   .0486472     2.07   0.040     .0048295    .1961243
                                                _cons |   .7188633   .0895302     8.03   0.000     .5428341    .8948926
-----------------------------------------------------------------------------------------------------------------------

. 
. est store reg1 

. 
. reg Justifiable ib0.LocalContributionsAttribute  ib0.ServiceProvisionAttribute ServiceProvisionAttribute#LocalContributionsAttribute ib0.FairnessA
> ttribute ib0.TransparencyAttribute FairnessAttribute#TransparencyAttribute ib0.DetectionAttribute ib0.FineAttribute DetectionAttribute#FineAttribu
> te ib0.GenderAttribute ib0.EarningsAttribute if EnoughIncome==1, robust 

Linear regression                               Number of obs     =        391
                                                F(11, 379)        =       0.66
                                                Prob > F          =     0.7737
                                                R-squared         =     0.0185
                                                Root MSE          =     .50063

-----------------------------------------------------------------------------------------------------------------------
                                                      |               Robust
                                          Justifiable | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------------------------------------+----------------------------------------------------------------
                          LocalContributionsAttribute |
                                      Large Donation  |  -.0305268   .0729408    -0.42   0.676    -.1739462    .1128926
                                                      |
                            ServiceProvisionAttribute |
                                         Good Access  |   .1127688   .0731664     1.54   0.124    -.0310942    .2566318
                                                      |
ServiceProvisionAttribute#LocalContributionsAttribute |
                          Good Access#Large Donation  |  -.0502348   .1016224    -0.49   0.621    -.2500492    .1495796
                                                      |
                                    FairnessAttribute |
                                             Equally  |   .0035656    .070549     0.05   0.960    -.1351508     .142282
                                                      |
                                TransparencyAttribute |
                                            A Lot Of  |  -.0284846   .0765269    -0.37   0.710     -.178955    .1219858
                                                      |
              FairnessAttribute#TransparencyAttribute |
                                    Equally#A Lot Of  |   .0099862   .1027471     0.10   0.923    -.1920396     .212012
                                                      |
                                   DetectionAttribute |
                                              Likely  |   -.038511   .0748399    -0.51   0.607    -.1856644    .1086425
                                                      |
                                        FineAttribute |
                                          Large Fine  |  -.0195085   .0730373    -0.27   0.790    -.1631176    .1241007
                                                      |
                     DetectionAttribute#FineAttribute |
                                   Likely#Large Fine  |  -.0244168   .1022208    -0.24   0.811    -.2254077    .1765741
                                                      |
                                      GenderAttribute |
                                              Female  |  -.0233217   .0518086    -0.45   0.653      -.12519    .0785466
                                                      |
                                    EarningsAttribute |
                                         120 000 KSH  |  -.0318644   .0511144    -0.62   0.533    -.1323677     .068639
                                                _cons |   .4951359   .0949615     5.21   0.000     .3084185    .6818533
-----------------------------------------------------------------------------------------------------------------------

. 
. est store reg2

. 
. reg TrustGov ib0.LocalContributionsAttribute  ib0.ServiceProvisionAttribute ServiceProvisionAttribute#LocalContributionsAttribute ib0.FairnessAttr
> ibute ib0.TransparencyAttribute FairnessAttribute#TransparencyAttribute ib0.DetectionAttribute ib0.FineAttribute DetectionAttribute#FineAttribute 
> ib0.GenderAttribute ib0.EarningsAttribute if EnoughIncome==1, robust 

Linear regression                               Number of obs     =        392
                                                F(11, 380)        =       2.32
                                                Prob > F          =     0.0091
                                                R-squared         =     0.0547
                                                Root MSE          =     .48713

-----------------------------------------------------------------------------------------------------------------------
                                                      |               Robust
                                             TrustGov | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------------------------------------------+----------------------------------------------------------------
                          LocalContributionsAttribute |
                                      Large Donation  |  -.1470408   .0694339    -2.12   0.035    -.2835635   -.0105181
                                                      |
                            ServiceProvisionAttribute |
                                         Good Access  |   .0891818    .072521     1.23   0.220    -.0534109    .2317746
                                                      |
ServiceProvisionAttribute#LocalContributionsAttribute |
                          Good Access#Large Donation  |   .1524346   .0986883     1.54   0.123     -.041609    .3464783
                                                      |
                                    FairnessAttribute |
                                             Equally  |  -.0484578   .0705992    -0.69   0.493    -.1872718    .0903562
                                                      |
                                TransparencyAttribute |
                                            A Lot Of  |  -.0417483   .0753504    -0.55   0.580    -.1899043    .1064077
                                                      |
              FairnessAttribute#TransparencyAttribute |
                                    Equally#A Lot Of  |   .1841377   .1000033     1.84   0.066    -.0124914    .3807668
                                                      |
                                   DetectionAttribute |
                                              Likely  |  -.0135022   .0710659    -0.19   0.849    -.1532339    .1262294
                                                      |
                                        FineAttribute |
                                          Large Fine  |   .0308155   .0705736     0.44   0.663    -.1079481    .1695791
                                                      |
                     DetectionAttribute#FineAttribute |
                                   Likely#Large Fine  |   .0114518   .0985387     0.12   0.908    -.1822976    .2052013
                                                      |
                                      GenderAttribute |
                                              Female  |  -.0313566   .0501174    -0.63   0.532    -.1298988    .0671856
                                                      |
                                    EarningsAttribute |
                                         120 000 KSH  |  -.0260416   .0497255    -0.52   0.601    -.1238132    .0717301
                                                _cons |   .4234837   .0932556     4.54   0.000     .2401221    .6068454
-----------------------------------------------------------------------------------------------------------------------

. est store reg3

. 
. *Combining all regressions into one table
. esttab reg1 reg2 reg3 using interactionsinc.tex, se label replace booktabs ///
> alignment (D{.}{.}{-1}) ///
> title( Interactions Eligible Income Sample \label{Table:interactionsinc})
(output written to interactionsinc.tex)

. 
. *----------------------------Table A10 
. 
. * The MDEs were manually calculated by multiplying 2.8 by the standard errors of the estimates in table 2 as per the process described in appendix
>  section B.2. 
. 
. capture log close
